Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

A critical review of the existing Internal Combustion Engine (ICE) modeling, optimization,
diagnosis, and control challenges and the promising state-of-the-art Machine Learning (ML) …

Being able to spot defective parts is a critical component in large-scale industrial
manufacturing. A particular challenge that we address in this work is the cold-start problem …

Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit very strong
anomaly detection performance when applied to entire images. A limitation of kNN methods …

Abstract The Internet of Things (IoT) is a complex and diverse network consisting of resource-
constrained sensors/devices/things that are vulnerable to various security threats …

Anomaly detection methods require high-quality features. In recent years, the anomaly
detection community has attempted to obtain better features using advances in deep self …

Anomaly detection, finding patterns that substantially deviate from those seen previously, is
one of the fundamental problems of artificial intelligence. Recently, classification-based …

Abstract Machine Learning (ML) has been enjoying an unprecedented surge in applications
that solve problems and enable automation in diverse domains. Primarily, this is due to the …

Novelty detection is the problem of identifying whether a new data point is considered to be
an inlier or an outlier. We assume that training data is available to describe only the inlier …

Anomaly detection is critical given the raft of cyber attacks in the wireless communications
these days. It is thus a challenging task to determine network anomaly more accurately. In …